Mostrar el registro sencillo del ítem

dc.contributor.author
Videla, Santiago
dc.contributor.author
Guziolowski, Carito
dc.contributor.author
Eduati, Federica
dc.contributor.author
Thiele, Sven
dc.contributor.author
Gebser, Martin
dc.contributor.author
Nicolas, Jacques
dc.contributor.author
Saez Rodriguez, Julio
dc.contributor.author
Schaub, Torsten
dc.contributor.author
Siegel, Anne
dc.date.available
2017-03-22T19:31:13Z
dc.date.issued
2015-09
dc.identifier.citation
Videla, Santiago; Guziolowski, Carito; Eduati, Federica; Thiele, Sven; Gebser, Martin; et al.; Learning Boolean logic models of signaling networks with ASP; Elsevier Science; Theoretical Computer Science; 599; 9-2015; 79-101
dc.identifier.issn
0304-3975
dc.identifier.uri
http://hdl.handle.net/11336/14203
dc.description.abstract
Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge networkand the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in siliconumerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A–B.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Answer Set Programming
dc.subject
Signaling Transduction Networks
dc.subject
Boolean Logic Models
dc.subject
Combinatorial Multi-Objective Optimization
dc.subject.classification
Ciencias de la Información y Bioinformática
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Learning Boolean logic models of signaling networks with ASP
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2016-12-16T17:27:04Z
dc.journal.volume
599
dc.journal.pagination
79-101
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Videla, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquimicas de Buenos Aires; Argentina. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia. Universität Potsdam; Alemania
dc.description.fil
Fil: Guziolowski, Carito. CNRS. École Centrale de Nantes; Francia
dc.description.fil
Fil: Eduati, Federica. European Bioinformatics Institute. European Molecular Biology Laboratory; Reino Unido
dc.description.fil
Fil: Thiele, Sven. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia
dc.description.fil
Fil: Gebser, Martin. Universität Potsdam; Alemania
dc.description.fil
Fil: Nicolas, Jacques. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia
dc.description.fil
Fil: Saez Rodriguez, Julio. European Bioinformatics Institute. European Molecular Biology Laboratory; Reino Unido
dc.description.fil
Fil: Schaub, Torsten. Universität Potsdam; Alemania
dc.description.fil
Fil: Siegel, Anne. CNRS. UMR; Francia. Campus de Beaulieu. Dyliss project; Francia
dc.journal.title
Theoretical Computer Science
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0304397514004587
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://dx.doi.org/10.1016/j.tcs.2014.06.022


Archivos asociados

Documento no disponible

Comunidades y colecciones

Mostrar el registro sencillo del ítem